The premise of a cognitive radio (CR) is to increase the utilization of airwave spectra for radio frequency (RF) communications by using locally unoccupied channels or frequency bands for unlicensed wireless communications. The specific details governing operation of CR have been defined by standards bodies such as the IEEE 802.22 group and CogNeA alliance.
Central to the operation of CR communications devices is the detection of an incumbent Advanced Television Systems Committee (ATSC) digital television (DTV) signal that may reside in the same frequency range as CR devices depending on geographic locale. Indeed, prior to using any frequency band, it is crucial that a CR device verify that the frequency band is not occupied by an ATSC DTV signal. Otherwise, the CR device's communications signal could interfere with the DTV signal and thereby prevent television reception in sets in close proximity to the CR device. Thus, it is necessary that the CR device be able to sense or detect DTV signals at power levels that could be otherwise received in the absence of a CR device as defined by standards and government regulations. This establishes the a key requirement of DTV sensing methods and systems—that they be able to sense weak DTV signals, such as signals as low as 10 dB below the thermal noise floor, i.e. −116 dBm, as defined by the IEEE 802.22-05/0007r47 functional requirements document.
In addition to requiring a CR to sense a channel or frequency band prior to communications use, a CR device must periodically sense the frequency band during use to account for a possible change in environment. Such a change is intended to account for the possibility of an erroneous “missed detection” of a DTV signal due to channel fading on the initial detection operation. The periodic sensing would also account for the movement of a CR device from a location absent of a DTV signal to a location with a DTV signal.
The periodic sensing aspect imposes another key requirement of DTV detection methods and systems—that they perform their sensing operation in as short a time as possible. Rapidity is essential because sensing cannot occur simultaneously with communications since the CR communications signal would otherwise drown out or mask the DTV signal. Consequently, the fraction of time spent performing DTV signal detection directly takes away from time that could be used for communications and therefore degrades throughput efficiency.
Yet another requirement of DTV sensing methods and systems is that they be flexible to the amount of ambient background noise radiation. In practice, the amount of background noise can be affected by many factors and in particular by nearby electronics and communications devices. Viable DTV sensing techniques need to be able to maintain a pre-specified false alarm rate (FAR) or equivalently probability of false alarm (PFA), e.g. PFA=10% according to the IEEE 802.22 group, regardless of the background noise level. Techniques that are not invariant to the background noise level can easily exceed this false alarm rate as large noise spurs trigger incorrect DTV detection classifications.
A fourth key aspect of a commercially viable DTV sensing solution is that it be of reasonable cost and hence complexity. Like all consumer electronics, cost is a key factor in success and a sensing solution which satisfies the first three technical key requirements is moot if it is not commercially viable.
A variety of methods have been proposed in the prior art for the task of DTV signal detection, but these methods are inadequate as they do not satisfy one of the key requirements mentioned above. For example, as described by Zeng and Liang in “Covariance based sensing algorithms for detection of DTV and wireless microphone signals,” IEEE document 802.22-06/0187-01-0000, Nov. 10, 2006, and “Maximum-Minimum Eigenvalue Detection for Cognitive Radio,” IEEE Symp. PIMRC, 2007, spectral energy-based detection methods can be effective for quickly detecting DTV signals, but these methods have to make prior assumptions on the background noise level, and when the noise level estimate is even slightly erroneous, detection performance decreases dramatically. Thus, Zeng and Liang propose covariance matrix based methods, but these methods can become quite computationally intensive as (i) the computation of a covariance matrix is an intensive task and (ii) eigenvalue based techniques involve matrix inversions which are extremely computationally intensive tasks.
Another category of signal detection techniques exploit the signal property of cyclostationarity developed by Gardner and summarized in “Exploitation of Spectral Redundancy in Cyclostationary Signals,” IEEE SP Magazine, April 1991, pp. 14-36; “Spectral Correlation of Modulated Signals: Part I—Analog Modulation,” IEEE Trans. On Comms., vol. 35, no. 6, June 1987, pp. 584-594; and W. Gardner, et al., “Spectral Correlation of Modulated Signals: Part II—Digital Modulation,” IEEE Trans. On Comms., vol. 35, no. 6, June 1987, pp. 595-601. In those works, Gardner proposes the use of cyclic autocorrelation or spectral correlation density (SCD) as the detection test statistic to exploit higher-order signal structure. However, as noted by Enserink and Cochran in “A Cyclostationary Feature Detector,” IEEE Asilomar Conf Proc., October 1994, pp. 806-810, Gardner's use of cyclic autocorrelation or SCD yields test statistics in which the false alarm rates are not invariant to the level of background noise, thus violating at least the third key aspect of DTV sensing for CR applications. To remedy this shortcoming, Enserink and Cochran propose a system that estimates the spectral auto-coherence function (SACF) evaluated at spectral frequency fc−ν and cyclic frequency α/2. In other words, it computes the cyclic-correlation between frequencies fc−ν+α/2 and fc−ν/2. A major drawback to the approach proposed by Enserink and Cochran is however that system precludes making full use of the entire sensing time interval to maximize frequency resolution, i.e. coherent averaging.
Thus, there is a need in the industry for systems, methods, and apparatuses for detecting DTV communications signals that remedy one or more of the above-described deficiencies or yet other deficiencies.